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‎2.Moive Recommendation.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Moive Recommendation"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## This project is to practice data structures, methods and functions of the Pandas and Numpy"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The goal of the project is to create movie recommendations for a person, based on the person’s and critics’ ratings of the movies. \n",
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"\n",
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"The following files will be required to run the program:\n",
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"1. `IMDB.csv`: A table with movie information\n",
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"2. `ratings.csv`: A table with ratings of all movies listed in the movies data \n",
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" by 100 critics. The column names in the critics data correspond to the name of each critic.\n",
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"3. `pX.csv`: A table with one person’s ratings of a subset of the movies in the movies data set, \n",
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" where X is a number. The column name in the file indicates the name of the person.\n",
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" \n",
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" \n",
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"All personal ratings are integer numbers in the 1..10 range."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"** How does this program function:** <br>\n",
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"1. The user will be asked to specify the `subfolder` in the current working directory, where the files are stored, along with the `names of the critics`, `person` and `movies data files`.\n",
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"2. Determine and output the names of three critics, whose ratings of the movies are closest to the person’s ratings based on the `Euclidean distance` metric.\n",
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"3. Use the `ratings by the critics` identified in item 2 to determine which movies to recommend. Display information about recommended movies as described below.<br>\n",
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"a. The movie recommendations must consist of the top-rated movies in each movie genre, based on the average ratings of movies by the three critics identified in step 2 above.<br>\n",
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"b. Movie genre is determined by the Genre1 column of the movies data.<br>\n",
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"c. Recommendations must be listed in alphabetical order by genre.<br>\n",
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"d. Missing data (e.g. running time) should not be included."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os.path\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"\n",
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"def main():\n",
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" '''\n",
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" The main function that is called to start the program. \n",
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" '''\n",
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" filesNames = input('Please enter the name of the folder with files, the name of movies file,\\\n",
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" \\nthe name of critics file, the name of personal ratings file, separated by spaces:\\n')\n",
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" print() #print a new line\n",
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" filesNamesLst = filesNames.split(' ') \n",
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" currentWorkDir = os.getcwd()\n",
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" subfolderName = filesNamesLst[0]\n",
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" #create a DataFrame for movies with selected columns\n",
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" movieFileName = filesNamesLst[1] \n",
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" movieFilePath = os.path.join(currentWorkDir, subfolderName, movieFileName)\n",
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" movieDataFrame = pd.read_csv(movieFilePath, \\\n",
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" encoding = 'unicode_escape').loc[:, ['Title', 'Genre1', 'Year', 'Runtime']] \n",
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" #create a DataFrame for critics ratings\n",
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" criticsFileName = filesNamesLst[2] \n",
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" criticsFilePath = os.path.join(currentWorkDir, subfolderName, criticsFileName)\n",
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" criticsDataFrame = pd.read_csv(criticsFilePath) \n",
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" #create a DataFrame for personal ratings\n",
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" personalFileName = filesNamesLst[3] \n",
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" personalFilePath = os.path.join(currentWorkDir, subfolderName, personalFileName)\n",
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" personalDataFrame = pd.read_csv(personalFilePath) \n",
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" #call functions to run the program\n",
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" topThreeCriticsLst = findClosestCritics(criticsDataFrame, personalDataFrame) \n",
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" print(topThreeCriticsLst, '\\n') \n",
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" movieRecommendation = recommendMovies(criticsDataFrame, personalDataFrame, \\\n",
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" topThreeCriticsLst, movieDataFrame)\n",
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" personName = personalDataFrame.columns[1]\n",
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" printRecommendations(movieRecommendation, personName)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"def findClosestCritics(criticsDataFrame, personalDataFrame):\n",
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" '''\n",
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" This function is to return a list of three critics, whose ratings of movies are most similar to \n",
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" those provided in the personal ratings data based on Euclidean distance. The lower the distance, \n",
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" the closer, thus more similar, the critic's ratings are to the person's. \n",
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" \n",
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" Parameters:\n",
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" criticsDataFrame - provides data about critics ratings\n",
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" personalDataFrame - provides data about personal ratings \n",
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" '''\n",
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" \n",
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" # merge critics file and personal file by the same movie title\n",
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" criticsPersonRating = pd.merge(criticsDataFrame, personalDataFrame) \n",
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" # a new DataFrame with only critics' ratings after merging without Title column\n",
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" criticRating = criticsPersonRating.iloc[:,1:-1] \n",
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" # indexed by the movie titles\n",
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" criticRating.index = criticsPersonRating['Title'] \n",
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" # person's rating value without the person's name\n",
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" personRatingValue = criticsPersonRating[personalDataFrame.columns[1]] \n",
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" # to keep the index the same as the critics' rating DataFrame \n",
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" personRatingValue.index = criticsPersonRating['Title'] \n",
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" ratingDifference = criticRating.sub(personRatingValue, axis = 0)\n",
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" eucliDistance = np.sqrt((ratingDifference**2).apply(np.sum))\n",
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" eucliDistance.sort_values(inplace = True) # sort the result from smallest to largest\n",
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" # select only the top 3 critics with smaller Euclidean distance \n",
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" topThreeCritics = eucliDistance.iloc[:3] \n",
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" topThreeCriticsLst = list(topThreeCritics.index.values) # generate a list of the critics' names\n",
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" \n",
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" return topThreeCriticsLst"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"def recommendMovies(criticsDataFrame, personalDataFrame, topThreeCriticsLst, movieDataFrame): \n",
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" '''\n",
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" This function is to compute the top-rated unwatched movies in each genre category \n",
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" based on the average of the three critics' ratings\n",
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" \n",
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" Parameters:\n",
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" criticsDataFrame - provides data about critics' ratings\n",
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" personalDataFrame - provides data about personal ratings \n",
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" topThreeCriticsLst - a list of three critics, whose ratings of movies are most similar to \n",
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" those provided in the personal ratings data\n",
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" movieDataFrame - provides data about movies info\n",
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" '''\n",
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" # prepare the DataFrames for critics rating, person's rating and movie indexed by movie title.\n",
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" criticsDataFrame.index = criticsDataFrame['Title']\n",
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" criticsDataFrame = criticsDataFrame.iloc[:,1:]\n",
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" personalDataFrame.index = personalDataFrame['Title']\n",
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" personalDataFrame = personalDataFrame.iloc[:,1:]\n",
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" movieDataFrame.index = movieDataFrame['Title']\n",
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" movieDataFrame = movieDataFrame.iloc[:,1:]\n",
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" # prepare the unwatched movie DataFrame with average ratings \n",
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" # from the three critics whose ratings are similar to the person's\n",
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" unwatchedCriticRating = criticsDataFrame.loc[criticsDataFrame.index.difference(personalDataFrame.index)]\n",
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" topThreeCriticsRating = unwatchedCriticRating[topThreeCriticsLst]\n",
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" averageCriticsRating = round(topThreeCriticsRating.mean(axis = 1), 2)\n",
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" movieDataFrame['Average Rating'] = averageCriticsRating \n",
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" movieDataFrame.sort_values('Genre1', inplace = True)\n",
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" movieRecommendation = movieDataFrame[movieDataFrame.groupby(by = 'Genre1')['Average Rating'].\\\n",
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" transform(max) == movieDataFrame['Average Rating']]\n",
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" \n",
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" return movieRecommendation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"def printRecommendations(movieRecommendation, personName):\n",
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" '''\n",
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" This function is to printout all the recommended movies in alphabetical order by the genre.\n",
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" \n",
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" Parameters:\n",
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" movieRecommendation - provides data about critics' ratings\n",
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" personName - the person's name for whom the recommendation is made for\n",
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" '''\n",
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" print('Recommendations for ', personName, ':', sep = '')\n",
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" # get the longest title for formatting later\n",
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" moiveTitle = list(movieRecommendation.index.values)\n",
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" longestTitle = len(max(moiveTitle, key = len))\n",
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" # get each factor (i.e. title, genre etc.) and then print with designed format \n",
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" for row in range(len(movieRecommendation)):\n",
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" title = movieRecommendation.index[row]\n",
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" gener1 = movieRecommendation.loc[title]['Genre1']\n",
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" year = movieRecommendation.loc[title]['Year']\n",
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" runTime = movieRecommendation.loc[title]['Runtime']\n",
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" rating = movieRecommendation.loc[title]['Average Rating']\n",
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" if pd.isnull(runTime) != True:\n",
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" print('\"', title, '\" ', (longestTitle - len(title))*' ', \\\n",
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" '(', gener1, '), ', 'rating: ', rating, ', ', year, ', runs ', runTime, sep = '')\n",
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" else:\n",
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" print('\"', title, '\" ', (longestTitle - len(title))*' ', \\\n",
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" '(', gener1, '), ', 'rating: ', rating, ', ', year, sep = '')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Please enter the name of the folder with files, the name of movies file, \n",
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"the name of critics file, the name of personal ratings file, separated by spaces:\n",
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"data1 IMDB.csv ratings.csv p8.csv\n",
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"\n",
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"['Quartermaine', 'Arvon', 'Merrison'] \n",
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"\n",
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"Recommendations for Catulpa:\n",
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"\"Star Wars: The Force Awakens\" (Action), rating: 9.67, 2015, runs 136 min\n",
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"\"The Grand Budapest Hotel\" (Adventure), rating: 9.0, 2014, runs 99 min\n",
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"\"The Martian\" (Adventure), rating: 9.0, 2015, runs 144 min\n",
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"\"Kubo and the Two Strings\" (Animation), rating: 9.67, 2016\n",
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"\"How to Train Your Dragon\" (Animation), rating: 9.67, 2010\n",
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"\"Hacksaw Ridge\" (Biography), rating: 9.33, 2016, runs 139 min\n",
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"\"What We Do in the Shadows\" (Comedy), rating: 9.0, 2014\n",
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"\"Prisoners\" (Crime), rating: 8.33, 2013, runs 153 min\n",
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"\"Spotlight\" (Crime), rating: 8.33, 2015, runs 128 min\n",
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"\"The Perks of Being a Wallflower\" (Drama), rating: 9.67, 2012, runs 102 min\n",
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"\"Shutter Island\" (Mystery), rating: 8.33, 2010, runs 138 min\n"
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]
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}
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],
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"source": [
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"main()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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